AIMC Topic: Machine Learning

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Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction.

PLoS computational biology
With the great advancements in experimental data, computational power and learning algorithms, artificial intelligence (AI) based drug design has begun to gain momentum recently. AI-based drug design has great promise to revolutionize pharmaceutical ...

Enabling Legal Risk Management Model for International Corporation with Deep Learning and Self Data Mining.

Computational intelligence and neuroscience
In uncertain times, risk management is critical in keeping companies from acting rashly and wrongly, allowing them to become more flexible and resilient. International cooperative production project investment and operational risks are different from...

Neuromorphic computing for content-based image retrieval.

PloS one
Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency. Here, w...

Machine Learning for the Orthopaedic Surgeon: Uses and Limitations.

The Journal of bone and joint surgery. American volume
➤: Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedi...

Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset.

Scientific reports
The objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiological sensor data to accurately predict early risk of sepsis. The fusion AI model has two component...

Accommodating Multiple Tasks' Disparities With Distributed Knowledge-Sharing Mechanism.

IEEE transactions on cybernetics
Deep multitask learning (MTL) shares beneficial knowledge across participating tasks, alleviating the impacts of extreme learning conditions on their performances such as the data scarcity problem. In practice, participators stemming from different d...

Learning With Selected Features.

IEEE transactions on cybernetics
The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) algorithm is excluded in the innov...

Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization.

IEEE transactions on cybernetics
Multivariate time-series prediction is a challenging research topic in the field of time-series analysis and modeling, and is continually under research. The echo state network (ESN), a type of efficient recurrent neural network, has been widely used...

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU.

Cardiology in the young
BACKGROUND: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient ca...

Machine learning strategy identification: A paradigm to uncover decision strategies with high fidelity.

Behavior research methods
We propose a novel approach, which we call machine learning strategy identification (MLSI), to uncovering hidden decision strategies. In this approach, we first train machine learning models on choice and process data of one set of participants who a...